The rapidly increasing volume of data collected for decision
support applications in commercial, industrial, medical, and defense
domains has made it a challenge to scale up knowledge discovery in
databases (KDD), the machine learning and knowledge acquisition
component of these applications. Many techniques currently applied
to KDD admit enhancement through the WRAPPER approach, which uses
empirical performance of inductive learning algorithms as feedback
to optimize parameters of the learning system.

Wrappers include algorithms for performance tuning, especially:
optimization of learning system parameters (HYPERPARAMETERS) such as
learning rates and model priors; control of solution size; and change
of problem representation (or inductive bias optimization).
Strategies for changing the representation of a machine learning
problem include decomposition of learning tasks into more tractable
subproblems; feature construction, or synthesis of more salient or
useful input variables; and feature subset selection, also known as
variable elimination (a form of relevance determination).

This workshop will explore current issues concerning wrapper
technologies for KDD applications.

WORKSHOP AUDIENCE

This workshop is intended for researchers in the area of machine
learning, including practitioners of knowledge discovery in databases
(KDD) and statistical and computational learning theorists. Intelligent
systems researchers with an interest in high-performance computation
and large-scale, real-world applications of data mining (e.g., inference
and decision support) will also find this workshop of interest.

CALL FOR PAPERS

We encourage submissions containing original theoretical and applied
concepts in KDD. Experimental results are also encouraged, especially
on fielded applications, even if they are only preliminary.
We therefore invite two categories of paper submissions:
- research papers
- short summaries (including position papers)

For the workshop agenda, submission procedure, and up-to-date
information on the review committee and invited speakers, please
visit the workshop web site:
www.kddresearch.org/KDD/Workshops/IJCAI-2001/

The rapidly increasing volume of data collected for decision
support applications in commercial, industrial, medical, and defense
domains has made it a challenge to scale up knowledge discovery in
databases (KDD), the machine learning and knowledge acquisition
component of these applications. Many techniques currently applied
to KDD admit enhancement through the WRAPPER approach, which uses
empirical performance of inductive learning algorithms as feedback
to optimize parameters of the learning system.

Wrappers include algorithms for performance tuning, especially:
optimization of learning system parameters (HYPERPARAMETERS) such as
learning rates and model priors; control of solution size; and change
of problem representation (or inductive bias optimization).
Strategies for changing the representation of a machine learning
problem include decomposition of learning tasks into more tractable
subproblems; feature construction, or synthesis of more salient or
useful input variables; and feature subset selection, also known as
variable elimination (a form of relevance determination).

This workshop will explore current issues concerning wrapper
technologies for KDD applications.

WORKSHOP AUDIENCE

This workshop is intended for researchers in the area of machine
learning, including practitioners of knowledge discovery in databases
(KDD) and statistical and computational learning theorists. Intelligent
systems researchers with an interest in high-performance computation
and large-scale, real-world applications of data mining (e.g., inference
and decision support) will also find this workshop of interest.

CALL FOR PAPERS

We encourage submissions containing original theoretical and applied
concepts in KDD. Experimental results are also encouraged, especially
on fielded applications, even if they are only preliminary.
We therefore invite two categories of paper submissions:
- research papers
- short summaries (including position papers)

For the workshop agenda, submission procedure, and up-to-date
information on the review committee and invited speakers, please
visit the workshop web site:
www.kddresearch.org/KDD/Workshops/IJCAI-2001/